American Journal of Plant Sciences, 2011, 2, 408-415
doi:10.4236/ajps.2011.23046 Published Online September 2011 (http://www.SciRP.org/journal/ajps)
Genotype-by-Environment Interaction and Yield
Stability Analysis in Finger Millet (Elucine
coracana L. Gaertn) in Ethiopia
Asfaw Adugna1*, Tesfaye Tesso2, Erenso Degu1, Taye Tadesse1, Feyera Merga1, Wasihun Legesse3,
Alemu Tirfessa3, Haileselassie Kidane1, Andualem Wole4, Chemeda Daba5
1Ethiopian Institute of Agricultural Research, Melkassa Agricultural Research Center, Nazareth, Ethiopia; 2Department of Agronomy,
Kansas State University, Manhattan, USA; 3Ethiopian Institute of Agricultural Research Pawe Agricultural Research Center, Pawe,
Ethiopia; 4Amhara Agricultural Research Institute, Adet Agricultural Research Center, Adet, Ethiopia; 5Oromia Agricultural Re-
search Institute, Bako Agricultural Research Center, Bako, Ethiopia.
Received April 18th, 2011; revised May 14th, 2011; accepted June 11th, 2011.
ABSTRACT
Finger millet is one of the mo st neglected and underutilized crop s worldwide, yet an important food cereal for millions
of poor farmers in Africa. An experiment was carried out to determine adaptation range of diverse set of finger millet
accessions and identify superior types with excellent yield potential for use as cultivar or as germplasm source for fu-
ture breeding endeavors. A total of 44 indigenous accessions selected in previous evaluations and two check varieties
were tested in two sets (mixed and colored) each containing 22 entries in a total of 11 environments between 2004 and
2008 seasons. Data were collected on grain yield, days to flowering, and plant height. The result showed that 2.5%,
79.1% and 18 .3 % of the total sum of squares in the mixed set and 2.1%, 86.9% and 11.0% in the colored set was at-
tributed to genotype, environment, and genotype × environment interaction (GEI) effects, respectively. Furthermore,
54.6% and 46.19% o f the GEI sum of squares in th e mixed and in the co lored set, respectively, were contributed by the
first two interaction principal component axes (IPCA1 and IPCA2). A white seed accession (Acc. 203572) from the
mixed set and three other accessions (Acc. 229469, Acc. 203410 and Acc. 203539) from the colored set were most sta-
ble and also had above average mean grain yield across environment and thus are recommended for release as culti-
vars to improve finger millet production in these environments.
Keywords: AMMI, Finger Millet, Genotype, Environment, Stability
1. Introduction
Finger millet (Eluc ine coracana L . Gaer tn), a membe r of
the Poaceae (Gramineae) family, is one of the most im-
portant food cereals in the sub-Saharan Africa and south
Asia. It is the third most widely cultivated millets after
pearl millet (Pennisetum glaucum) and foxtail millet
(Setaria italica) in the semi-arid tropical and subtropical
regions of the world [1]. Indigenous to eastern Africa,
finger millet is widely produced in the cool high altitude
areas in the region primarily as source of food and also
for making traditional alcoholic beverages [2]. In Ethio-
pia, the crop is mainly grown in the northern, north
western and western parts of the country, especially dur-
ing the main rainy season. Finger millet is often mixed
with other grain crops such as tef or sorghum to make
composite flour for local food preparation such as injera
and porridge. It is often valued as nutritious cereal by
local people. This ob servation has scientific merit in that
finger millet contains relatively higher concentration of
calcium and dietary fiber than other cereals [3].
Notwithstanding its importance, pub lished information
is scarce on the agronomy and genetics of the crop. In
Ethiopia, finger millet occupies 4% of the total area al-
located to cereals (nearly half a million hectares) each
year and also contributes about 4% to the total annual
cereal grain production in the country [4]. Similar to tef,
finger millet grain can be stored for several years under
local storage conditions without sustaining significant
damage by storage pests [5,6]. This property together
with its adaptation to low input conditions and relatively
better nutritional value [7] makes it one of the salient
crops among resource poor communities living in food

Genotype-by-Environment Interaction and Yield Stability Analysis in Finger Millet 409
(Elucine coracana L. Gaertn) in Ethiopia
insecure areas [8]. In Ethiopia, it is often grown in poor
soils without fertilizer, and thus the national average
yield rarely exceeds 1 ton per hectare.
Although formal research to improve the crop has
started some three decades ago, not much progress has
been made because of funding limitation as the crop is
not among the priority commodities. As a result, only
two varieties have been identified for cultivation to date
but appropriate management practices are still lacking.
Though the varieties were initially released for cultiva-
tion in the sub-humid and mid altitude areas, their inad-
vertent introduction in to low rainfall areas found new
adaptation zones. At present the production of these va-
rieties has expanded to dry low altitude areas including
regions where the crop was previously unknown [5].
Frustrated by repeated failure of the maize crop as a re-
sult of frequent drought, farmers in the dry Rift Valley
region of Ethiopia widely adopted the variety that it is
currently grown as one of the most important crops in
this region [9].
Encouraged by the expanded adoption, the Ethiopian
national sorghum research program increased its effort to
identify additional high yielding varieties that can fit in
to a wide range of environments. This effort drew an
important lesson from past activities where extensive
evaluation of hundreds of entries involving exotic
sources acquired through the Eastern African Regional
Sorghum and Millet (EARSAM) research network pro-
duced only limited progress. Hence, as of 2003 much of
the focus was placed on evaluation of local sources for
adaptation and yield potential. Superior genotypes se-
lected from different stages of screening were pulled
together and evaluated at multiple locations representing
different agro-ecologies. Therefore, this paper discusses
the performance of these genotypes under a range of en-
vironments and generates information on the extent of
genotype-by-environment interaction which is useful in
designing suitable approaches for variety selection.
2. Materials and Methods
The experiment was conducted from 2004 through 2008
in the main rainy seasons at four locations (Adet, Arsi
Negelle, Bako and Pawe) in eleven environments. Major
characteristics of the test environments are presented in
Table 1.
2.1. Genetic Materials
A total of 44 finger millet landraces, selected from tests
conducted in previous years, were evaluated in this study.
The materials were grouped in to two sets each contain-
ing 22 entries. Majority of the test entries were from se-
lections made among the 2003 observation nursery that
contained a pool of landrace collections received from
the Ethiopian Institute of Biodiversity Conservation
(IBC). The grouping was made to reduce the number of
genotypes in each set and thus maximize uniformity
among experimental units. Hence, the materials were
arbitrarily assigned to the two groups with the ten white
seeded genotypes purposely placed in the first set to al-
low within group comparison among white seeded en-
tries. This set is designated as “mixed set”. All genotypes
assigned to the second set have colored grains (copper,
light red, dark red, brown, black) and hence were re-
ferred to as “colored set”. Moreover, two released varie-
ties (Tadesse and Padet) were included in both sets to
serve as standard check.
Table 1. Major geo-climatic characteristics of the test environments.
Temperature (˚C)
Location Year
Environment
code‡ Position Altitude (m) Soil type Mean annual rain fall (mm) Min. Max.
Adet 2004 A N11˚16', E37˚29' 2060 1250 7.8 25.4
Arsi-Negele 2004 B N7˚19', E38˚39' 1960 Vertisol 870 11 21
2005 D
2006 F
2007 H
2008 K
Bako 2007 I N9˚8', E37˚03' 1550 Nitosol 1178 13.2 28
Pawe 2004 C N11˚18', E36˚24' 1050 Vertisols/Fluvisols1580 15 32.4
2005 E
2006 G
2007 J
‡ As the environments were common to bo th sets of tria ls in a single season, the codes are the same (e.g., A = Adet in 2004 in both trials).

Genotype-by-Environment Interaction and Yield Stability Analysis in Finger Millet
410 (Elucine coracana L. Gaertn) in Ethiopia
2.2. Experimental Setup
The experiment for both sets was laid in a randomized
complete block design with four replications in all loca-
tions and seasons. Because there were no recommended
spacing and fertilizer rate developed for finger millet, a
blanket recommendation adopted from sorghum was
used. Each plot consisted of three 5 m long rows spaced
0.75 m apart. The seeds were manually drilled into each
row and latter thinned to a spacing of 15 cm between
plants. Trials in all environments received Diammonium
phosphate fertilizer applied at a rate of 100 kg·ha–1 at
planting. In order to avoid lodging, nitrogen fertilizer
was not applied in all environments. The field was kept
free of weeds throughout the testing seasons. Harvesting
and threshing were done manually.
2.3. Data Collection and Analysis
Data were recorded on grain yield (kg·ha–1), days to 50%
flowering, (from emergence to the time when half of the
plants in the plot bloomed) and plant height (cm) (from
the ground level to the tip of the longest finger) in all
environments. Data on grain was recorded when the
moisture content was reduced to 12.5%. Moreover, the
accessions were visually evaluated for their reaction to
lodging and b last. The data were subjected to analysis of
variance (ANOVA) for each of the environments and for
the combined data using SAS 9.1 (SAS Institute). More-
over, Additive Main Effects and Mu ltiplicative In teraction
(AMMI) ANOVA and AMMI biplot were performed
using CropStat 7.2 Software [10]. The additive main ef-
fects and multiplicative interaction (AMMI) model is a
multivariate approach proposed to dissect the GEI in to
two main components. The first component is the AN-
OVA, which is the additive component and the second is
the interaction principal components [11]. The AMMI 1
biplot contains main effect (genotype/environment) means
in the x-axis and the first interaction principal component
axis (IPCA 1) in the y-axis such that genotypes and/or
environments that appear in a perpendicular line have
similar means and those that appear on a horizontal line
have similar interaction patterns [12]. Further, stable
genotypes (with less GEI) are those, which have IPCA 1
values closer to zero regardless of their sign. Therefore,
the best genotypes are those, which are placed on the
right side of the AMMI 1 biplot origin (the junction of
IPCA 1 at zero and the average mean yield) marked at or
closer to the IPCA 1 origin (zero).
3. Results
3.1. Grain Yield and Phenology
The AMMI ANOVA for the combined data is presented
in Table 2. Genotype, environment, genotype × envi-
ronment interaction effects were significant for grain
yield and days to flower ing in both sets. In the mixed set
experiment, 2.5%, 79.1%, and 18.3% of the total sum of
squares was attributed to genotypes, environments, and
genotype × environment interaction effects. The result
for the colored set was also similar to the mixed set and
showed that much of the observed variability (86.9%)
was attributed to the environmental variance and only
2.08% and 11.02% of the total sum of square for yield
could be explained in terms of genotype and genotype ×
environment interaction, respectiv ely.
Table 2. Analysis of variance for the AMMI model for grain yield.
Mixed set Colored set
Source of variation D.F. S.S. % contribution S.S. % contribution
Genotypes (G) 23 8599920 2.53 7631910 2.08
Environments (E) 10 269182000 79.13 318627000 86.90
G × E Interaction 230 62402300 18.34 40403500 11.02
IPCA 1 32 24182100 38.75 11504100 28.47
IPCA 2 30 9858900 15.80 7156500 17.71
IPCA 3 28 8486000 13.60 6506620 16.10
IPCA 4 26 6609360 10.59 5684490 14.07
G × E residual 114 13266000 9551830
Total 263 340184000 366662000

Genotype-by-Environment Interaction and Yield Stability Analysis in Finger Millet 411
(Elucine coracana L. Gaertn) in Ethiopia
The mean grain yield of genotypes included in the mixed
set ranged from 2074 kg·ha–1 to 2804 kg·ha–1 in Acc.
203523 and Acc. 203564, respectively. Fourteen of the 24
genotypes had above average yield, but only Acc. 203564
had significantly higher yield than the entry mean (2541
kg·ha–1) (Table 3). Moreover, in the same set, mean grain
yield among environments ranged from 1230 kg·ha–1 to
4416 kg·ha–1 in E and B in that order. Yield at six of the
eleven environments was higher than average.
In the colored set, genotype yield ranged from 2369
kg·ha–1 in Acc. 203319 to 3217 kg·ha–1 in Acc. 203539.
Eleven of the 24 genotypes included in this set showed
above average performance (Table 4). However, only
three of them: Acc. 229469, Acc. 203410, and Acc.
203539, had significantly higher yield than the entry mean.
Seven and three of the genotypes in this set out yielded the
check varieties Tadesse and Padet, respectively. Similarly,
the mean yield among the environments ranged from 1479
kg·ha–1 in G to 4698 kg·ha–1 in B. Only four of the eleven
environments, B, D, F and K, supported yields significantly
higher than the overall mean. In both sets of experiments,
the standard variety Padet out yielded the other standard
Tadesse. In several locations, accessions in both sets had
yields that were significantly higher than both standard va-
rieties but none of the across location mean yield of the
mixed set genotypes was significantly higher than the stan-
dard va rietie s.
Table 3. Mean grain yield (Kg·ha–1), days to 50% flowering (DTF), plant height (PH), and the joint regression (bi) of the
mixed set finger millet landrace accessions tested in 11 environments.
Grain yield-by-environment
Genotypes** A B C D E F G H I J K Mean DTF PHbi
1. Acc. 229345 1988 33112677 2645 1636418014043422195416842444 2486 96 103.10.75
2. Acc. 229349(W) 2441 4347 1948 2289 828 39763494433 1060737 2311 2247 97 106.71.35*
3. Acc. 229367 1817 42222587 3467 169446789503400166618352911 2657 96 106.11.02
4. Acc. 229380(W) 2986 51562242 3733 592 336713294556143317593444 2782 97 104.61.25
5. Acc. 229401 1947 43782504 2911 1498345616613867279121402822 2725 98 100.10.79
6. Acc. 229463(W) 2638 4511 2069 2578 405 3484 1788 47111821826 2667 2500 101 112.21.19
7. Acc. 229465(W) 2711 47112138 1533 922 40111275262258516073389 2319 99 110.51.10
8. Acc. 229470 2648 51562279 2889 373 344428744133124210842800 2629 97 107.31.16
9. Acc. 203358(W) 2406 46672068 2245 470 33561296393367523333000 2404 100 111.11.13
10. Acc. 203402 2250 44002574 2889 1626432214673296202218202444 2646 98 99.60.9 1
11. Acc. 203509 2172 46442562 2578 1733341613893933219218672889 2670 97 98.90.90
12. Acc. 203523 1778 4067716 1667 988 41331 0592933163717022133 2074 100 108.00.99
13. Acc. 203530(W) 2251 45781775 2378 451 35442535464419761 0002978 2555 102 117.31.12
14. Acc. 203542 2448 48672584 3000 1946381712853533193320252644 2735 97 102.40. 93
15. Acc. 203562 2298 43562371 3133 168529449373222238017322911 2543 96 98. 40.78
16. Acc. 203564 2427 46672527 3200 1906431115573489253117532478 2804 97 105.80. 91
17. Acc. 203572(W) 2866 52802102 3355 996 44911409266714741 7563089 2680 96 98.71.16
18. Acc. 203587(W) 2719 4778 2073 1778 411 5344 1446 3900 1807979 2622 2532 99 112.51.39*
19. Acc. 203558 2227 47782590 2778 164941569213751185112862378 2578 97 105.91.11
20. Acc. 215986 1714 31113053 1578 2451370014522711246821312456 2439 102 94.80.40*
21. Acc. 215869(W) 2780 4244 2228 1889 650 3867 1266 4033 1755781 2867 2396 101 109.51.15
22. Acc. 215962(W) 2531 4311 1702 1711 790 4033 1617 4089675772 2689 2265 101 112.21.23
23. Tadesse 2541 36221749 3556 1983380415993356181920922822 2631 97 108.00.69*
24. Padet 2358 38223008 2734 1832342220243200191721553044 2683 97 105.90.60*
Mean 2372 44162255 2605 1230388614543660173615772760 2541 98 105.8
LSD (0.05) 477.3 1288 497 927 321 1578 617966668393 802.6 258 8 9.0
CV (%) 14.25 20.6815.6 25.2 18.528.830. 118.727.217.620.6 24 5 12.7
*slopes sig nificantly different from 1.00 (the slope for the overall regression), **W = accessions with white kern el color, t he rest are brown.

Genotype-by-Environment Interaction and Yield Stability Analysis in Finger Millet
412 (Elucine coracana L. Gaertn) in Ethiopia
Table 4. Mean grain yield (Kg·ha–1), days to 50% flowering (DTF), plant height (PH), and the joint regression (bi) of the col-
ored set finger millet landrace accessions tested in 11 environments.
Grain yield-by-environment
Genotypes A B C D E F G H I J K Mean DTF PH bi
1. Acc. 229376 2497 4311 3063 5211 1789422013972228195420563400 2921 93 101.51.02
2. Acc. 229381 2147 4578 2661 4833 1846350513642484119420822867 2687 93 103.00.987
3. Acc. 229383 2604 4045 2292 4022 2027432215271984166622583578 2757 91 100.50.857
4. Acc. 229398 2902 4156 2482 5411 1389426711131774143317602911 2691 93 108.01.175
5. Acc. 229399 2652 4867 2027 4722 1568407115612058279122893245 2895 91 101.40.982
6. Acc. 229400 2630 3978 2727 4045 2414366524392405182122622289 2788 91 99.80.594*
7. Acc. 229407 2792 4489 2643 4244 2417425313042042585 27542867 2763 94 101.40.971
8. Acc. 229415 2944 4845 2207 4445 2485393317702093124226863156 2891 94 104.40.941
9. Acc. 229417 2876 5289 2256 4889 1990440011061670595 21623622 2805 92 99.51.318*
10. Acc. 229440 2884 5178 2054 4873 1455361317591459202218241956 2643 88 105.41.088
11. Acc. 229442 2797 4933 2264 4800 1723346712761444219219823334 2746 94 110.11.068
12. Acc. 229458 3172 4511 2416 4667 2080389613991340162923113556 2816 93 104.71.026
13. Acc. 229461 3199 4978 2437 4211 2098420013402120197526303000 2926 94 106.60.95
14. Acc. 229462 2842 4022 2206 4613 2164313616032025193324513000 2727 90 104.60.774*
15. Acc. 229468 2909 5222 2093 4545 1940342214221616237921723289 2819 92 109.01.011
16. Acc. 229469 2810 5022 2303 5656 1840424415371719282719113533 3036 91 111.61.169
17. Acc. 203410 3330 4756 2534 5444 2086404514032246147422394089 3059 92 104.61.142
18. Acc. 203539 3100 5511 2541 4578 3627384712472334180735723222 3217 90 85.50.914
19. Acc. 203289 2767 4267 2005 4656 1742324713561887185124892978 2658 94 99.00.895
20. Acc. 203300 2347 4511 2067 4456 18354531163217082468750 2800 2646 95 103.41.02
21. Acc. 215961 2341 4134 2301 4889 1746404515381709146018533156 2652 94 99.21.026
22. Acc. 203319 2759 5200 2138 3111 898 3756 1740 1434830 1180 3011 2369 93 106.91.048
23. Tadesse 2623 5022 2403 4434 1621335614711429181916362822 2603 92 106.81.038
24. Padet 2661 4934 2763 3933 1608422512011820191721963045 2755 95 102.10.99
Mean 2774 4698 2370 4612 1933390314791876174421463113 2786 93 103.3
LSD (0.05) 495 865 618 1110 457 1235 591 613 653 494 915 232 3 6.6
CV (%) 12.62 13.04 18.5 17 16.822.428.323.126.516.320.8 19.65 3 9.8
*Slopes significantly different from 1.00 (the slope for the overall regression).
Days to flowering ranged from 96 to 102 in the mixed
set, and from 88 to 95 in the colored set. Similarly, the
range for plant height was 94.5 cm to 117.3 cm in the
mixed set and 85.5 cm to 111.6 cm in the colored set.
Plant height (r1 = –0.36, r2 = –0.34) and days to flower-
ing (r1 = –0.53, r2 = –0.23) were found to have negative
correlation with grain yield.
3.2. Response and Stability of the Landraces
Genotypes, Acc. 229349 and Acc. 203587 from the
mixed set had linear regression coefficient significantly
higher than 1.0 (the overall regression) and hence were
highly responsive to the suitable environments (Table 3 ).
However, since they are tall accessions (Table 2), adding
more inputs may enhance lodging. On the other hand,
Acc. 215986, Tadesse and Padet had slopes significantly
lower than 1.0 and hence were b etter adapted to marginal
environments.
The AMMI analysis showed that all of the 4 principal
component axes were significant in both sets. However,
54.6% and 46.19% of the GEI sum of squares in the
mixed set and in the colored set, respectively, were con-
tributed by the first two interaction principal components
(IPCA1 and IPCA2). Five accessions in the mixed set, Acc.

Genotype-by-Environment Interaction and Yield Stability Analysis in Finger Millet 413
(Elucine coracana L. Gaertn) in Ethiopia
229465, Acc. 203509, Acc. 203523, Acc. 203572, and
Acc. 203558 were shown to have the highest stability as
revealed by their relative position with respect to the
biplot origin (Figure 1). However, none of these acces-
sions had significantly higher yield than the overall entry
mean and the check varieties. Among the colored set,
five accessions, Acc. 229458, Acc. 203410, Acc. 203289,
Acc. 215961, Acc. 203319, and the check variety Padet
showed better stability than the rest of the en tries (Figure
2). Again none of these accessions did exceed the stan-
dard checks except Acc. 203410 that produced signifi-
cantly higher yield than both check varieties. This acces-
sion is also within the same range of maturity (days to
flowering) and height group with that of the standard
varieties.
4. Discussion
In general, the genotypic variation in the studied traits
was considerably narrow probably because of the rigor-
ous selection process conducted in the previous year
which might have not intentionally targeted these traits.
The influence of GEI resulted in variable performance of
the genotypes in the different test environments. Varie-
ties with high levels of heterozygosity and/or heteroge-
neity are less sensitive to env ironmental variatio n and are,
therefore, more stable-yielding. On the other hand, the
Elucines generally are reported to be strictly autogamous
with low levels of heterozygosity. This is perhaps the
major factor that contributed to the high GEI in finger
millet in the present study.
Figure 1. AMMI 1 Biplot of the 24 finger millet varieties and the 11 test environments in the mixed set.

Genotype-by-Environment Interaction and Yield Stability Analysis in Finger Millet
414 (Elucine coracana L. Gaertn) in Ethiopia
Figure 2. AMMI1 Biplot of the 24 finger millet varieties and the 11 test environments in the colored set.
While positive correlation between days to flower-
ing/maturity and yield seems to be a common phenome-
non in crop plants, the negative correlation in the present
experiment was perhaps due to the concomitant occur-
rence of late flowering with the suitable period of fungal
(blast) infection that reduces yield. Moreover, the nega-
tive correlation between plant height and grain yield
might be due to lodging. Taller plants tend to lodge more
than shorter ones and lo se their yield . Some of the testing
sites (especially Pawe and Bako) have high rainfall and
high temperature, which is suitable for the development
of fungal diseases like blast (Pyricularia spp.) that re-
duce yield more on the lodged plants.
Various models to measure stability of genotype per-
formance across multi-environments are available in lit-
erature. At present, the most widely used model is
AMMI, which involves both ANOVA and principal
component analysis to dissect GEI into the causes of
variation. However, stability per se is not necessarily a
positive factor and it is desirable only when associated
with a high mean yield (Yan and Hunt, 2002). In the
present experiment, a white seed accession (Acc. 203572)
from the mixed set and three other accessions (Acc.
229469, Acc. 2 03410 and Acc. 2 03539) fro m the color ed
set were found to be most stable based on the AMMI
model and also had above average mean grain yield
across environments and thus are recommended for re-
lease as cultivars to contribute for enh anced finger millet
production in these environments. The response of
Tadesse to the poor environments in the first set was in
agreement with the previous observation during the scal-
ing up activity in the dry lowland areas of the Ethiopian

Genotype-by-Environment Interaction and Yield Stability Analysis in Finger Millet 415
(Elucine coracana L. Gaertn) in Ethiopia
rift valley (Siraro and Alaba). However, a similar re-
sponse was not observed in the other set because coeffi-
cient of joint regression (bi) is a relative measure, which
varies with the genotypes included in the set [13].
In the past decade, 2001-2010, finger millet production
area in Ethiopia increased from 342,120 ha to 368,9 99 ha
with an increase of 7.3%, and the production increased
from 3,769,290 to 5,241,911 quintals with a proportion
of 28% [4,14]. This was partly due to the adoption of
improved varieties and production practices or possibly
an indication of the fact that agriculture is being pushed
to the more marginal areas due to the associated change
in climate demanding adaptable crops. Thus, a continu-
ous supply of high yielding varieties that have stable per-
formance in a wide range of environments is needed for
sustainable production. To this end, we believe that the 4
genotypes selected in this experiment will have signifi-
cant contribution to enhance production in areas where
there is similar agro-climatic conditions with the test
environments.
In conclusion, east Africa is reported to be a region of
contrasts, where Africa’s lowest and highest elevations
are found; the differences of which coupled with the dif-
ferences in rainfall and temperature over short geo-
graphic distances provided varying environments suitable
for crop diversification, early domestication and subse-
quent cultivation of landraces. In Ethiopia, diverse forms
of finger millet landraces are found in altitude ranges of
around 500 m (e.g. Chikumbo) to 2500 m (e.g. South
Gondar). However, selection of high yielding and stable
genotypes in nation wide multi-environments has not
been successful. While finger millet can be a potential
cereal for food security under the rapidly changing cli-
mate, alleviating its constraints will remain a challenging
task for the researchers. In addition to the prevailing
production constraints of finger millet, which are mainly
related to poor management practices, some more are
still emerging. For instance, in northern Ethiopia, the
parasitic weed, Striga spp. is expanding its host range
from maize and sorghum, its principal hosts to small ce-
reals, tef and finger millet. Hence, exhaustive work
should be done on identifying the landraces and side by
side introduction and evaluation of exotic germplasm.
Moreover, no agronomic recommendations such as spa-
cing and fertilizer rate are available to date for finger
millet in the country. Therefore, multidisciplinary work
is binding in order to break the yield barriers and to reap
the potential from these untapp ed genetic resources.
5. Acknowledgements
We thank the sorghum and millets technical staff at Melkas-
sa, Arsi Negelle, Pawe, Adet and Bako Research Centers.
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